Setup

Getting Started

First we need to get the data from the European Social Survey using the ESS package. I am using the “needs” package to make installing and loading packages easier.

library(needs)
needs(essurvey)

While we are at it, let us also load a few other packeges that we will need for the analysis.

  needs(tidyverse)  #Tidyverse helps us with many helper functions 
  needs(ggplot2)    #for nice graphics
  needs(car)        #I still clean my data using car
  needs(plotrix)    #For standard error function
  needs(ggthemes)   #To make ggplot look nicer
  needs(plotly)     #To make ggplot animated
  needs(crosstalk)  # To enable highlighting

Downloading the Data

The data on punitivness can be found in ESS waves 4 (2008) and 5 (2010). We can download the data directly from within R, using the “essurvey” package. You can download more than one wave at once, but to facilitate data cleaning we download each wave seperately. You will need to register your email with the ESS first.

    set_email("cgnguyen@gmail.com") #Add your own email here
    DATA_4<-import_rounds(4)
    DATA_5<-import_rounds(5)

Cleaning the Data

First we need to clean our data. This means removing missing values, making sure that the coding is the same across each waves, and sometimes inversing scales.

Note that this data is already well prepared, so we can rely on one simple command to remove missing variables. Also going to save a vector of variable names that makes merging it easier later.

    DATA_4<-recode_missings(DATA_4)
    DATA_5<-recode_missings(DATA_5)
    
    length_vec_4<-length(DATA_4)
    length_vec_5<-length(DATA_5)

Dependent Variables: Punitivness

For now we look at one variable that looks at how much punishment people “deserve”. Specifically, the question asks “People who break the law deserve much harsher sentences”. This question is coded on a scale from 1(agree strongly) to 5 (disagree strongly) - we will reverse this scale so that higher values indicate more punitativness and have it range from 0 to 4.

Note that the variable is named slightly differently in wave 4 and 5

  DATA_4$punish<-(DATA_4$hrshsnt-5)*-1
  DATA_5$punish<-(DATA_5$hrshsnta-5)*-1

Clean individual level independent variables

We control for sociodemographic variables such as gender, age, main activity, level of education, and household income. We need to think about what kind of measure of economic insecurity we care about. We could go with labor market status, income, or maybe even labor market risk using ISCO rates.

We make use of the fact that the data is already labeled - this means we can use the “as_factor” command to turn the numerical variables into factor variables that R understands.

  DATA_4$gender<-as_factor(DATA_4$gndr)
  DATA_4$age<-DATA_4$agea
  DATA_4$activity<-as_factor(DATA_4$mnactic)
  DATA_4$education<-as_factor(DATA_4$eisced)
  DATA_4$income<-as_factor(DATA_4$hinctnta)
  
  DATA_4$cntry<-as_factor(DATA_4$cntry)
  
  DATA_5$gender<-as_factor(DATA_5$gndr)
  DATA_5$age<-DATA_5$agea
  DATA_5$activity<-as_factor(DATA_5$mnactic)
  DATA_5$education<-as_factor(DATA_5$eisced)
  DATA_5$income<-as_factor(DATA_5$hinctnta)
  DATA_5$cntry<-as_factor(DATA_5$cntry)

Merging the Dataset

We select the variables from the dataset that we need and then merge them into one new dataset for analysis. We need to do this because each survey has different questions, so we cannot simply combine the two. We also “drop” the empty levels of factor variables

select.vec<-c("punish","gender","age","activity","education","income","cntry")

DATA_4_merge<-DATA_4[,select.vec]
DATA_5_merge<-DATA_5[,select.vec]

DATA<-rbind(DATA_4_merge,DATA_5_merge)
DATA<-droplevels(DATA)

I am going to be using a lot of graphs in this. To make sure I don’t have to define every graph theme anew, I am going to create a new theme that sets some baselines for the figures we will produce. We will start with the basic minimalist theme, rotate the x-axis by 90 degrees and change the background to white.

  theme_awesome <- theme_bw() 
  
  theme_awesome<-theme_awesome +
      theme(legend.position = "none")
  
  theme_set(theme_awesome)

Descriptive Statistics

Very Basic results

Before we start our “proper” analysis, it is always a good idea to look at the data first and see what it looks like. We should really be adjusting for survey and design weights, but are going to ignore this for now to make the code easier to read.

Let us begin by looking at the distribution of our dependent variable.

summary(DATA$punish)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   2.000   3.000   2.901   4.000   4.000    2925

So on average, most people want to have higher punishments for crime, with the median answer being “agree”. These averages are interesting, but let’s see if these differ by a few key few factors. In line with our theory, let us look at different forms of economic risk - unemployment and household income.

By Main Activity

To do this, we use dplyr to quickly calculate mean values per group + standard errors around them. We use the plotrix package for a quick helper function for the calculation of standard errors and ggplot2 to graph these.

There is a clear and signfiicant difference between people who are unemployed and people who are in paid work. However, there are other groups that score even higher on their relative punitive score. Retired people are the most punitive, while people in education are the lowest. This suggests that age may be a confounder here. Those who are permanently sick or disabled similarly score much higher, as do people who are looking after children.

By Income Group

By Education Group

By Country

This might be better with a map

Crime and Punishment (and Welfare States)

Introduction